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Texture-Based Flow Visualization Daniel Weiskopf Tutorial: State-of-the-Art Flow Field Analysis and Visualization Texture-Based Flow Visualization Daniel Weiskopf IEEE VIS 2013 | Atlanta | 2013-10-13 Overview . Background . Line integral convolution and texture advection . Hierarchical line integration . Effective rendering and feature extraction . Outlook and conclusion Flow Visualization Approaches . Direct approaches . Glyphs, arrows . Color coding . Sparse, line-like representations . Dense, line-like representations . Characteristic lines . Streamlines . Pathlines . Streaklines [courtesy of BMW Group and Martin Schulz] Visualization Approaches Arrows Sparse (topology) Dense (texture-based) [Source: G. Scheuermann] [Weiskopf, Erlebacher 2005] Visualization Approaches Arrows Sparse (topology) Dense (texture-based) [Source: G. Scheuermann] see later: Vector Field Topology in Flow Analysis and Visualization [Weiskopf, Erlebacher 2005] Visualization Approaches Arrows Sparse (topology) Dense (texture-based) [Source: G. Scheuermann] [Weiskopf, Erlebacher 2005] Why Texture-Based Flow Visualization? . Dense sampling . Better coverage of information . (Partially) solved problem of seeding . Flexibility in visual representation . Good controllability of visual style . From line-like (crisp) all the way to fuzzy Dense Vector Field Visualization . Characteristic lines . Challenges . Visualization speed . Quality . Effectiveness . Interactive GPU methods . Hierarchical computation . Facilitate good visual perception . Combine with feature extraction [Weiskopf et al. 2003] Ingredients of Vector Field Visualization Advection (Transport mechanism) Rendering VIS Properties (What is advected? Mapping to primitives) Line Integral Convolution (LIC) Convolution I(x(t s))k(s)ds Noise I Vector field Particle tracing Kernel k(s) -L L Result Lagrangian Particle Tracing . Velocity vector v . Trace massless particles dr v(r,t) . Equation of motion: dt . Explicit integration . Step-by-step . Possible in GPU fragment shaders see later: Foundations of Data-Parallel Particle Advection Convolution . Discretization of the convolution integral . Integration simultaneously with particle tracing . Accumulate noise values Mapping to GPU: Parallel Computation Convolution Fragment shader Noise texture Data set in texture Kernel in 1D texture -L L Render to texture Incremental LIC: Texture Advection [Van Wijk 2002] . Injection of new material advected injection . IBFV idea texture texture . Injection texture compositing . Alpha blending advection step . Exponential filter kernel (steady case) . Streaklets (unsteady) [generated by Van Wijk’s demo program] Golf of Mexico Data set: 352 x 320, 183 time steps [Data courtesy of O‘Brien, FSU] [Weiskopf et al. 2005] 2D and 3D LIC . 2D case: . Only 2D textures (vector field, noise, result) . Rendering is trivial: just 2D image . 3D case: . 3D textures for vector field and noise . Slice-by-slice output . Volume rendering . 3D alternative: . On-the-fly computation [Falk, Weiskopf 2008] . Integrated within GPU ray casting . Lazy evaluation: output sensitivity 3D LIC On-the-Fly [Falk, Weiskopf 2008] 3D LIC On-the-Fly [Falk, Weiskopf 2008] Hierarchical Line Integration [Hlawatsch et al. 2011] . Coordinate maps 푛 휑푖 : 퐷 → 퐷 , 퐷 ⊆ ℝ 푥 : start point of traj. 휑2: 휑푖(푥) : end point of traj. 휑1: : nodes 푖 : hierarchy level 휑0: . 휑0 obtained, e.g., by integration . 휑>0 constructed by “concatenation” . All levels have same resolution (no pyramid) . Overwrite (store only highest level) • general case: end points not at nodes interpolation Hierarchical Line Integration: Procedure traditional approach hierarchical approach (n integration steps) (h levels) integration of initial trajectories 휑0 O(n) one catenation (s = 2) for next level 휑+1 O(h) = O(log n) Hierarchical Convolution . Perform LIC operations inside hierarchical scheme . Combine intermediate quantities from integration . Convolution of Gaussian with Gaussian is Gaussian straightforward hierarchical [Hlawatsch et al. 2011] Performance of Hierarchical Integration [Hlawatsch et al. 2011] Time-Dependent Hierarchical Integration . level 0: from data set (by integration, blue) . green: required for result at time t1 (at level 3) . bold outlines: blocks kept in memory (overwrite) . hatched: next time blocks . integration range number of blocks in memory . scheme pays off for time series, i.e., dense trajectory seeding in time . no temporal interpolation needed [Hlawatsch et al. 2011] 2.5D LIC . Adapted to flow on boundaries . Image-space methods [Van Wijk 2003], [Laramee et al. 2003], [Weiskopf, Ertl 2004b] Object space Projection Particle trace Image plane Eye [Weiskopf, Ertl 2004b] 2.5D LIC [Weiskopf, Ertl 2004b] On Pathsurfaces [Image: Schafhitzel et al. 2007] al.et Schafhitzel [Image: [Schafhitzel et al. 2007] On Pathsurfaces [Image: Schafhitzel et al. 2007] al.et Schafhitzel [Image: [Schafhitzel et al. 2007] Effective Visual Representation . Perceptual issues for 3D LIC and texture advection . Spatial perception: Orientation, depth . Clutter . Occlusion Improved Rendering . Improve spatial perception [Interrante, Grosch 1997] . Illumination . Halos . Depth cues . Line continuity [Image courtesy of Victoria Interrante] [Courtesy of Interrante, image reprinted from Weiskopf, Erlebacher 2005] Spatial Perception . Technical solution: Real-time volumetric illumination [Weiskopf et al. 2007], [Falk, Weiskopf 2008] . On-the-fly computation of gradients . Various illumination models (Phong, cool/warm, halos) . Tangent-based illumination Spatial Perception . No illumination [Weiskopf et al. 2007] Spatial Perception . Phong illumination [Weiskopf et al. 2007] Spatial Perception . Cool/warm [Weiskopf et al. 2007] Codimension-2 Illumination . Illuminated streamlines: no gradients computed [Falk, Weiskopf 2008] Codimension-2 Illumination . Alternative codimension-2 illumination model (Mallo) [Falk, Weiskopf 2008] Codimension-2 Illumination . Comparison: without illumination [Falk, Weiskopf 2008] Codimension-2 Illumination . Comparison: gradient-based illumination [Falk, Weiskopf 2008] Clutter and Occlusion [Weiskopf, Ertl 2004a] Clutter and Occlusion [Weiskopf, Ertl 2004a] Clutter and Occlusion [Weiskopf, Ertl 2004a] Different Noise Models: “Seeding” Dense (white noise) Sparse noise [Falk, Weiskopf 2008] Different Noise Models: “Seeding” Dense (white noise) Sparse noise see from before: Streamlines in 3D: Techniques beyond Seed Placement [Falk, Weiskopf 2008] Flow Feature Extraction . 3D interest function . Domain knowledge . Interactive exploration Vortex extraction with 2 [Weiskopf et al. 2007] Flow Feature Extraction . 2.5D visualization on 2 isosurfaces [Schafhitzel et al. 2006] Flow Feature Extraction . 3D LIC with 2 feature enhancement [Falk, Weiskopf 2008] Clipping and Semi-Transparency [Rezk-Salama et al. 1999] Outlook: Topics Not Covered Here . Physically oriented dye advection . Advection and diffusion [Karch et al. 2012] . Numerical quality of dye and texture advection . Level-set and particle level-sets 2012] al. et [Karch [Weiskopf 2004b], [Cuntz et al. 2008] . WENO schemes [Karch et al. 2012] . Higher-order and BFECC advection [Netzel et al. 2012] . Quality of filtering . Frequency analysis: low-pass filter characteristics [Netzel et al. 2012] , [Weiskopf 2009] [Netzel et al. 2012] al. et [Netzel Outlook: Topics Not Covered Here . Relationship to dense geometric curves and hybrid techniques [Verma et al. 1999], [Weiskopf et al. 2005] ] . Control of rendering styles . Chameleon system [Li et al. 2003] . Non-uniform grids and higher-order reconstruction et al. 2003 [Li . Perceptual graphics . Texture, color, motion perception [Bachthaler, Weiskopf 2008], [Weiskopf 2004a] 2004a] Weiskopf [ Conclusion . Texture-based vector field visualization . Flexible, widely applicable . Fast . Components . Transport mechanism . Visual representation and rendering . Visualization quality Further Material www.vis.uni-stuttgart.de/texflowvis References [Bachthaler, Weiskopf 2008] S. Bachthaler, D. Weiskopf: Animation of orthogonal texture patterns for vector field visualization. IEEE Transactions on Visualization and Computer Graphics 14(4), 741-755, 2008. [Cuntz et al. 2008] N. Cuntz, A. Kolb, R. Strzodka, D. Weiskopf. Particle level set advection for the interactive visualization of unsteady 3D flow. Computer Graphics Forum 27(3), 719-726, 2008. [Falk, Weiskopf 2008] M. Falk, D. Weiskopf. Output-sensitive 3D line integral convolution. IEEE Transactions on Visualization and Computer Graphics 14(4), 820-834, 2008. [Hlawatsch et al. 2011] M. Hlawatsch, F. Sadlo, D. Weiskopf. Hierarchical line integration. IEEE Transactions on Visualization and Computer Graphics 17(8), 1148-1163, 2011. [Interrante, Grosch 1997] V. Interrante, C. Grosch. Strategies for effectively visualizing 3D flow with volume LIC. Proc. IEEE Visualization '97, 421-424, 1997. [Jobard et al. 2002] B. Jobard, G. Erlebacher, M. Y. Hussaini. Lagrangian-Eulerian advection of noise and dye textures for unsteady flow visualization. IEEE Transactions on Visualization and Computer Graphics 8(3), 211-222, 2002. [Karch et al. 2012] G. Karch, F. Sadlo, D. Weiskopf, C.-D. Munz, T. Ertl: Visualization of advection-diffusion in unsteady fluid flow. Computer Graphics Forum 3, 1105-1114, 2012. [Laramee et al. 2003] R. S. Laramee, B. Jobard, H. Hauser. Image space based visualization of unsteady flow on surfaces. In Proc. IEEE Visualization ’03, 131-138, 2003. References [Li et al. 2003] G.-S. Li, U.D. Bordoloi, H.-W. Shen. Chameleon: An interactive texture-based rendering framework
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